Systems, Methods and Devices for Managing/Controlling Energy Production and/or Energy Storage Systems

ABSTRACT

A method for controlling an energy storage system which includes receiving time-series data and customer specific data and developing one or more customer specific control models based, at least in part, on the time-series data and the customer specific data. After developing one or more customer specific control models, the method proceeds by training the customer specific control models and then deploying the customer specific control model to the customer for use by the customer to determine which of a plurality of modes the energy storage system should be in. The method may include the development, deployment and/or execution of one or more centralized control models for controlling a network of any combination of common and/or different customer control models.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.15/511,980, filed Mar. 16, 2017, which is the National Phase applicationof International Application No. PCT/AU2015/000573, filed Sep. 16, 2015,designates the United States and was published in English, which claimspriority to U.S. Provisional Application No. 62/051,597, filed on Sep.17, 2014. These applications, in their entirety, are incorporated hereinby reference.

FIELD OF THE DISCLOSURE

This disclosure relates to systems, methods, and devices for managingdistributed and/or non-distributed energy production and/or storagesystems. More particularly, this disclosure relates to systems, methods,and devices for managing/controlling distributed and/or non-distributedenergy production and/or storage systems by using combinations of timeseries data and customer specific data to manage/control the benefits ofenergy production and/or storage systems individually and as a networkof systems and improve power grid efficiency and/or stability.

BACKGROUND

Today, there are numerous alternatives to obtaining energy exclusivelyfrom the power grid. For example, many commercial and residentialpremises are utilizing alternative energy sources such as wind, solar orother energy sources to generate electricity. However, many of thesealternative energy sources are unreliable, inconsistent and/orintermittent. For example, the sun and the wind may vary from one day tothe next, making it difficult to rely exclusively on any one or more ofthese alternative energy sources. To assist, commercial and residentialpremises are increasingly installing energy storage systems (e.g.,batteries). Batteries are able to store energy, making it available asand when necessary. For example, solar energy received during the daycan be stored in energy storage systems and utilized later at night;wind energy can be stored during windy times and consumed in later lesswindy times; and so on.

In general, energy storage may have a multiplicity of uses. For example,energy storage systems may be utilized for energy arbitrage—the processof storing energy at times when it has low market value and selling orotherwise consuming it at times of higher value. Energy storage devicesmay also be useful for voltage or frequency correction on the grid. Forexample, the energy storage system may either rapidly discharge orcharge if it detects a change in frequency that might arise due to asudden loss of renewable generation or the loss of a load or a voltagechange. Energy storage devices may also be used to deliver power andenergy services directly to energy and ancillary services marketsthrough, for example, contractual arrangements.

However, despite the potential customer- and/or network-relatedbenefits, none of the many uses for energy storage on their own arelikely to render them sufficiently economically attractive to meritinstallation.

Accordingly, it is desirable to have systems, methods, and devices formanaging energy storage systems, which are capable of utilizing themultiplicity of capabilities of the energy storage systems to improvethe electrical and economic benefits of energy storage.

SUMMARY

Exemplary embodiments described herein may provide a method forcontrolling an energy storage system, the method comprising: receivingtime-series data and customer specific data; developing a customerspecific control model based at least in part on the time-series dataand the customer specific data; training the customer specific controlmodel; and deploying the customer specific control model to the customerfor use by the customer to determine which of a plurality of modes theenergy storage system should be in.

In exemplary embodiments, the mode of the energy storage system maycomprise a mode (e.g., charge, discharge, do nothing) and a magnitude ofthe action. Although the modes and magnitudes described herein may bedescribed separately, it should be well understood that in exemplaryembodiments, the modes and magnitudes may actually correspond to acontinuous spectrum of operations. It should be clear that a position onthe spectrum may be selected in a single operation or in multipleoperations. For example, in an exemplary embodiment, the modes may be asubstantially continuous set of values signifying whether to charge,discharge or do nothing (e.g., a series of positive values, negativevalues, and zero).

In exemplary embodiments, the plurality of modes of the energy storagesystem may comprise any combination of a charging mode, a dischargingmode, and a do nothing mode.

In exemplary embodiments, the plurality of modes of the energy storagesystem may further comprise any combination of producing or consumingreal and/or reactive power, or do nothing.

In exemplary embodiments, the plurality of modes of the energy storagesystem may further comprise an indication of the desired net flow ofenergy, comprising any combination of real and/or reactive, into or outof the energy storage system.

In exemplary embodiments, the customer specific control model may beconfigured to maximize a financial return from the energy storage systemby selling energy back to the network at commercially advantageous timesas determined by the customer specific control model. In exemplaryembodiments, the control model may be configured to minimize, maximize,or merely alter any number of given parameters in addition to financialreturn or market value.

In exemplary embodiments, the customer specific control model may beconfigured to correct for frequency deviations over a range of timescales.

In exemplary embodiments, the customer specific control model may beconfigured to correct for any combination of real and/or reactivevoltage or a voltage range by consuming or producing any combination ofreal or reactive power.

In exemplary embodiments, the customer specific control model may beconfigured to fulfill an energy related contract for producing orconsuming any combination of real or reactive power over a contractspecified period of time and geographic region.

In exemplary embodiments, any combination of common or unique customerspecific control models for a plurality of customers may be collectivelycontrolled by a centralized system to optimize the ability of the systemas a whole to fulfill the temporal, spatial and/or real and/or reactivepower needs of the power network, such as correcting for frequencydeviation, voltage deviation, fulfilling an arbitrary energy relatedcontract, etc.

In exemplary embodiments, the customer specific control model may beupdated by deploying an update of the model to the customer (e.g., bypushing the update).

In exemplary embodiments, the customer specific control model may bedeployed by physical connection to the controller of a portableelectronic storage device containing an updated control model.

In exemplary embodiments, the customer specific control model may bedeployed via a wireless communications channel, such as a mobilecellular network which may or may not include a secure connection suchas a virtual private network.

In exemplary embodiments, the customer specific control model may bedeployed via the customer's connection to a public communicationsnetwork via a wired (such as Ethernet) or wireless (such as WiFi)communications channel, which may or may not include a secure connectionsuch as a virtual private network.

In exemplary embodiments, the control model may utilize informationabout the customer configuration and/or characteristics to determinewhich of a plurality of modes the energy storage system should be in.

In exemplary embodiments, a different customer specific control modelmay be developed for different customers.

In exemplary embodiments, the time-series data may comprise anycombination of historical energy market information, environmentalinformation, electricity usage and/or generation from the commercial orresidential premises, weather services, and/or financial data related tomacro and micro economic conditions, and other time-series data.

In exemplary embodiments, the customer specific data may comprise anycombination of the location of the customer, the customer preferences,electricity retail contract information, network provider information,electrical details about the connection point, information about theconfiguration and characteristics of any energy generation, informationabout the energy storage system, and/or any other information as needed.

In exemplary embodiments, the data may be validated prior to beingutilized to develop the customer specific control model.

In exemplary embodiments, the energy storage system may comprise atleast one energy storage cell.

In exemplary embodiments, the customer specific control model maycomprise a plurality of modules which are configured to be executed in apredetermined manner based, at least in part, on a linked relationshipbetween the modules and output parameters and/or the type of eventreceived. Alternatively, in exemplary embodiments, the control model maybe dynamically implemented. For example, the model may be configured onthe fly during operation or reconfigured during operation or merelyswitched/replaced with an alternative model during operation.

Exemplary embodiments described herein may provide a system forcontrolling a plurality of energy storage systems associated with acorresponding plurality of customers, the system comprising: at leastone storage device configured to store time-series data and customerspecific data for a plurality of customers; at least one centralizedprocessor configured to: (i) receive the time-series data and thecustomer specific data for a plurality of customers; (ii) develop one ormore customer specific control models for a plurality of customersindividually or as one or more groups based at least in part on thetime-series data and the customer specific data of the correspondingcustomer; and (iii) train the customer specific control models; aplurality of controllers associated with a corresponding plurality ofcustomer energy storage systems, wherein the plurality of controllers isconfigured to receive a corresponding customer specific control modelsto determine which of a plurality of modes the energy storage systemcorresponding to the customer should be in.

In exemplary embodiments, the system may comprise one or a plurality ofcentralized models for centrally controlling or managing models for aplurality of customers where customers may or may not share the samemodel.

In exemplary embodiments, the system may comprise one or a plurality ofcentralized models centrally controlling or managing models for aplurality of customers to optimize the economic, electrical or otheroperation of a plurality of customer models as one or more groups.

In exemplary embodiments, the system may comprise one or a plurality ofcentralized models for centrally controlling or managing models for aplurality of customers where the centralized model or models may varyfrom time-to-time and may manage varying customers and customer modelsfrom time-to-time.

Other aspects, features, and advantages will become apparent from thefollowing description when taken in conjunction with the accompanyingdrawings, which are a part of the disclosure and which illustrate, byway of example, principles of the embodiments disclosed herein.

DESCRIPTION OF THE DRAWINGS

Notwithstanding any other forms which may fall within the scope of thedisclosure as set forth herein, specific embodiments will now bedescribed by way of example and with reference to the accompanyingdrawings in which:

FIG. 1 is an exemplary embodiment of a system for managing an energystorage system; and

FIG. 2 is an exemplary embodiment of a data table engine for use with asystem for managing energy storage systems.

DESCRIPTION OF EXEMPLARY EMBODIMENTS

Energy storage is a tool that can be used to solve several differentproblems faced by today's electricity grid. Despite its benefits,rendering electricity storage cost effective remains a significantchallenge.

Exemplary embodiments described herein relate to systems, methods anddevices which allow an energy storage system to manage a consumer'selectricity usage patterns, manage real and/or reactive load and voltageon the local grid, and trade on electricity markets, either individuallyor as part of a plurality of individual electricity consumers andproducers. By using energy storage devices to solve several problemssimultaneously, the systems and devices described herein improve theoperation and performance of the electricity grid and the economics ofenergy storage systems and electricity consumption more generally.

In most modern electricity grids, engineering requirements are convertedinto financial quantities which allow distinct generators, and amultiplicity of other energy participants, to work together in concert.For example, using centralized market mechanisms, a shortage ofelectricity supply relative to demand may manifest itself as a high spotprice. In turn, the high spot price attracts additional generation. Thesystems, devices, and methods described herein use these same financialsignals as the underlying basis for controlling one or more energystorage systems.

In electrical systems, it is vital that supply precisely matchesconsumption. When electricity grids were first designed there was no wayto control consumption so the system was designed to precisely controlgeneration. Consumption was essentially random but the aggregate loadwas well understood and could be easily met by the controllable mix offossil fuel, hydroelectric, and/or other power generation. This designwas successful for many decades. However, with the recent addition andgrowing popularity of distributed power production, such as rooftopsolar panels and large renewable farms (and other renewable energy), thesituation has been altered. Unfortunately the high geographiccorrelation of renewable energy means that consumption is no longerrandom. For example, a cloud front may pass many solar panelssimultaneously causing rapid fluctuations in solar power production, andhence consumption. Because the consumption is no longer random, theaggregate consumption may be less predictable than before. The lesspredictable loads make it increasingly difficult for the controlledgeneration to match this aggregate consumption. In addition to lesspredictability on the consumption side, it should be readily understoodthat as more alternative generation (e.g., wind, solar, etc.) isutilized, there may be less predictability on the generation side aswell.

In some geographies the traditional electricity production system isreaching its technical capacity to absorb more renewable energy andremain stable.

In exemplary embodiments, the systems, methods, and devices describedherein may reduce or eliminate these problems. In particular, thesystems described herein may connect behind-the-meter storage to gridcontrol systems. As a consequence it controls the behavior of thestorage in accordance with the needs of the system. If the systemdemands generation (as represented by a high spot price), then thedescribed system may respond by sending a discharge signal to thebattery. In an alternative situation, if solar panel generationunexpectedly drops off and causes a frequency dip, then a controlledenergy storage system may export any combination of real and/or reactiveenergy to the network to equalize the supply and demand of energy.

Although exemplary embodiments are described herein with reference toon-grid applications, it should be readily understood that similaroptimization may be achieved in an off-grid environment or if the energymarket is decentralized or manufactured.

In exemplary embodiments, the net result of this type of storage controlis that distributed energy production, such as solar, wind and othertime-varying forms of energy production, particularly those at the endof the grid such as roof-top panels, can work in harmony with thetraditional controlled generators.

In exemplary embodiments, the systems, methods and devices describedherein may deliver these benefits by working towards maximizingrevenues, e.g., participating in the most financially advantageouswholesale energy, ancillary services market, bilateral contract, etc. atany given moment.

For example, energy storage may be dispatched (e.g., real and/orreactive charge or discharge, or do nothing) in response to anycombination of the following conditions, including:

Price signals in the wholesale energy market or ancillary servicesmarket;

Price signals in the distribution network;

Operational conditions (capacity, faults, etc.) in the transmission ordistribution networks;

Generation capacity, constraints, and faults of generators connected tothe transmission or distribution networks;

Operational requirements, consumption, or usage patterns of anindividual residence or premises connected to the distribution network;

Price signals in the retail contract of an individual residence orpremises connected to the distribution network;

Environmental factors (temperature, wind speed, etc.); and/or

Contracts for energy storage services/financial contracts (derivatives)backed by storage capability.

Contracts to deliver real and/or reactive power in response to anexplicit request for real and/or reactive power of a specifiedmagnitude.

Contracts to deliver real and/or reactive power in response to anexplicit request based on a constraint in the distribution ortransmission network.

Accordingly, in exemplary embodiments, the dispatch of energy storage(e.g., using information from above sources) may be optimized tomaximize the financial return of one or more energy storage systems(e.g., by simultaneously satisfying the requirements of multiple energyand power services) operating individually or as a plurality of energystorage systems operating collectively.

Exemplary embodiments described herein may include systems, methods, anddevices for using time-series data such as, e.g., historical energymarket information, environmental information and other time-relatedinformation, along with customer-specific data to develop and/or testone or more computational models and/or control models (e.g.,computational graphs) to manage/control an energy storage system. Inexemplary embodiments, the energy storage system may be one or morebatteries, a battery management system (BMS) and an inverter. Inexemplary embodiments, the one or more batteries may be located at acustomer's premise (e.g., a home or office building).

In exemplary embodiments, the system may manage and/or control theenergy storage system to increase the benefit. For example, thecontroller may ensure that there is sufficient charge to delivervaluable services throughout the day by potentially charging overnightwhen electricity is typically cheaper. During the day it may identifythe most valuable opportunities, being mindful of the (potentially lost)opportunity cost of delivering other services during the day, anddeliver energy and power to satisfy those service requirements. Examplesof this are charging overnight when energy is cheap. Charging from solarin the morning when the sun first rises. Avoiding network charges in theafternoon when peak network pricing is in place, etc. For example, inembodiments, the system may control the energy storage system to improve(e.g., maximize) a financial return on the energy storage system. Inexemplary embodiments, the systems, methods, and devices describedherein may be capable of achieving up to about 10%, 20%, 30%, 40%, 50%,60%, 70%, 80%, 90%, 100%, 125%, 150%, 175%, 200%, 250%, 300%, or 400%increase in the financial return of the energy storage system.

In exemplary embodiments, the system may operate on individual energystorage systems or a plurality of energy storage systems may be pooledtogether and share a common model. For example, in exemplaryembodiments, individual commercial or residential premises in aneighborhood may have their own individual model or a single model maybe shared by the neighborhood. Exemplary embodiments described hereinmay also include one or more centralized systems, methods and devicesfor coordinating, managing, controlling, and/or optimizing thecollective control of a plurality of customers' control models, wheresuch customer control models may or may not comprise one or a pluralityof control models which may or may not be common to a plurality ofcustomers. Exemplary embodiments described herein may also include oneor more centralized systems, methods and devices for coordinating,managing, controlling, and/or optimizing the collective control of aplurality of customers' control models to optimize their control toachieve specific network and/or economic benefits from time-to-time,where such customer control models may or may not comprise one or aplurality of control models which may or may not be common to aplurality of customers.

In exemplary embodiments, the systems, methods and devices may beconfigured to develop customer-specific models and then deploy (e.g.,push) the model out to the respective customer's energy storagecontroller. In a similar manner, updates, revisions, corrections to themodels may also be deployed (e.g., push) to the respective energystorage controllers.

Along with the control model, in exemplary embodiments, specificcustomer configuration information may also be sent. In exemplaryembodiments, the specific customer configuration information may allowthe controller to operate in a parameterized fashion. This type of astructure may help ensure that while multiple customers may share acommon control model, each controller is unique in the control decisionsit delivers at each unique customer. For example, two customers livingnext door to one another may be in the same market region for wholesaleenergy, may be on the same network, may have the same retailer etc.However, these two customers may have different battery systems.Accordingly, the controller, which may be identical for both customers,may be parameterized according to the specifications of the specificbattery system. In this way it is possible the systems may make the samedecision (i.e. charge, discharge, do nothing) but the magnitude of thedecision may be different depending on the battery specification.

In operation, the systems, devices, and methods may transmit (e.g.,push) time-series data to the client controllers. In exemplaryembodiments, the time-series data may comprise energy and ancillarymarket prices, local weather data (or likely weather predictions forthat region), likelihood of network events if that data is available,etc. The client controllers may then utilize their control model and thedata to compute commands and/or actions to the energy storage system,the market, etc. In exemplary embodiments, the commands and/or actionsmay be performed in real time or at least substantially real time.

In exemplary embodiments, the systems, methods, and devices describedherein may be configured to analyze, manage, control, and/or monitorthousands or even hundreds of thousands of energy storage systems at thesame time where such energy storage systems may or may not share one ormore common control models.

In exemplary embodiments, the system may be configured for monitoringpurposes without battery control. For example, the system may providegraphs and data to customers but not control or manage an energy storagesystem. In exemplary embodiments, the system may analyze the data tosuggest system or behavioral changes to alter energy consumptionpatterns.

In exemplary embodiments, the system may be configured to include aconsumer application configured to display certain data to customers.For example, in exemplary embodiments, the consumer application maycomprise a user interface configured to display data relating to energygeneration, consumption, storage, and usage. The application may allowthe customer to monitor and/or modify energy consumption behaviors ormerely alter when energy is consumed. For example, it may be possible toconfigure the customer application to suggest to the customer, changesin activities that would reduce the amount of imported energy required(e.g., running the washing machine at a particular time, etc.).

In addition, applications/interfaces may be provided to retailers,network operators and/or customers to view particular statistics ondifferent systems. For example, in an exemplary interface, a map may beprovided with battery icons at locations where controllable energystorage systems (i.e. batteries) are located. It may be possible forusers of the interface to view statistics of the various systems,purchase options for dispatching energy, actually dispatch energy fromthe energy storage system into the electricity network, etc. Theinterface may also allow further control (in a variety of forms) of thevarious energy storage systems.

Exemplary embodiments described herein may provide systems, methods, anddevices that manage, monitor, and/or control energy storage systems thatare connected to the electricity grid. In embodiments, the system maycomprise (i) software/hardware that is located on a central architectureand interacts with existing market, network and vendor systems, (ii)software/hardware that is located on central architecture whichsimulates the value of deployed energy storage, automates the creationand deployment of embedded software to manage, optimize and controldeployed energy storage systems and/or monitors the performance ofdeployed energy storage against ideal simulations performed on eitherhistorical data or running in real time on the server but notcontrolling real hardware, and (iii) distributed controlsoftware/hardware which is deployed and physically located on a smallcomputer adjacent to or part of the energy storage system. The controlsoftware/hardware issues commands to the installed energy storage systemto command its operation (charge, discharge, do nothing) in order tomaximize (or at least improve) a return on investment.

In order to realize the value of energy systems installed on theelectricity market, in exemplary embodiments, the system may beconfigured to actively participate in an electricity market (e.g., TheNational Electricity Market (NEM) in Australia). To do this, inexemplary embodiments, a number of licenses may be required.

Through market participation or bi-lateral contracts the system may beable to generate a multiplicity of revenue streams subject to localmarket conditions including, but not limited to, for example, any one ormore of the following:

-   -   1. Wholesale market participation:        -   a. Energy arbitrage—buy power (charge battery) when the            price is low and sell power (discharge battery) when the            price is high    -   2. Buy and sell with the market operator or in an established        energy or capacity market:        -   a. Peak generation capacity when wholesale energy prices are            high        -   b. Frequency control services.            -   i. An example of which is frequency control ancillary                services (FCAS) in the NEM which includes the following                services:                -   1. 6 second raise                -   2. 60 second raise                -   3. 5 minute raise                -   4. 6 second lower                -   5. 60 second lower                -   6. 5 minute lower        -   c. System restart services.            -   i. An example of which is system restart ancillary                service (SRAS) in the NEM.    -   3. Services to Distribution and Transmission Networks:        -   a. Network support contracts—firmer alternative to demand            response/demand management (e.g., a the ability to supply            energy/power as needed by the network companies to overcome            local constraints, avoid replacing old transformers, etc.).        -   b. Voltage regulation, including reactive voltage support in            distribution networks.        -   c. Electricity feeder capacity support during electric            vehicle (EV) charging using stationary electricity storage            installed at a residence        -   d. Management of reverse current in distribution networks            through the tactical use of introducing load through            electricity storage charging    -   4. Creation of Financial Instruments:        -   a. Hedge contracts—Financial hedge provision using the            real-time aggregate and available state of charge from a            collection of storage systems as the counter-party to the            hedge.        -   b. Forward contracts for the provision of real or reactive            power potentially based on predicted available state of            charge from a collection of storage systems.        -   c. Derivative contracts for the provision of real or            reactive power potentially based on predicted available            state of charge from a collection of storage systems.        -   d. Option-Dispatch contracts—Contracts which include a price            for the option of dispatching energy, for a given duration,            during a particular time interval and then a price for            actually dispatching the energy storage system during that            time interval, for the requested duration.    -   5. Distribution network and other interested parties may        purchase the following:        -   a. Smart electricity meter data provision.    -   6. Black start capabilities—Provision of real or reactive power,        or energy, in order to re-energize the electricity network in        case a system-wide fault causes it to go ‘dark’.    -   7. The ability to control appliances at a commercial or        residential premises based, in part, on the state of the energy        storage system (e.g., air conditioners, lights, etc.)    -   8. The ability to provide home automation capabilities and        services as it relates to the control/use of the energy storage        system. (e.g., a market enabled power point, market enabled        lighting that determines the color of lights based on the        current price of energy.)    -   9. The ability to orchestrate the delivery of spatially (e.g.        geographically) and temporally correlated real and reactive        power, or energy, using a collection of energy storage systems.    -   10. Visualization of the operations of an energy storage system        for consumers.    -   11. Peer-to-peer based power and energy services—e.g., give        someone free energy from or sell spare energy to someone, or        share energy with someone (e.g., similar to family        grouping/sharing on mobile phone plans).    -   12. Ability to sell derivatives based on ancillary services        (frequency control, etc.)    -   13. Ability to sell derivatives based on current state of charge        of an energy storage system (or group of energy storage systems)    -   14. Ability to sell services to renewable energy generators that        increase the correlation between their generation profiles and a        targeted consumption profile.    -   15. Predictive modeling of consumption profile in residential        and commercial premises.    -   16. Ability to use ripple control for protection of commercial        or residential premises with energy storage systems.    -   17. Enable customers to make decisions about consumption or        energy storage system operation based on real time state of        charge and market information.    -   18. Ability to deliver services across multiple electricity        phases, or to balance energy, power, angle between phases.        Ability to provide precise time based energization of bus bars        across phases using multiple energy storage systems.    -   19. Probabilistic connection point modeling for electric        vehicles. Ability to operate storage in electric vehicles when        it is available and not otherwise. Potential to control it        across various geographies as it moves about.    -   20. Ability to operate energy storage systems to counteract        behavior of residential or commercial premises, in regards to        the grid.    -   21. Using waveform data obtained from a commercial off the shelf        meter to determine the current frequency of the grid and use        that to deliver frequency based services. This relates to the        provision of frequency control services where an approved        frequency trigger is necessary.

Additionally, to successfully participate in the energy markets, inexemplary embodiments, it may be desirable to understand how metering isperformed. For example, in many energy markets, energy usage is chargedover a period of time, not instantaneously. Accordingly, knowledge ofthe market time intervals may enable energy storage control software toreduce oscillatory loads and increase (or maximize) financial (or other)returns. In particular, because energy usage is charged over a period oftime (and the tariffs for import and export are not generally equal), itis possible to import and export energy at the same time and this may beless than optimal in certain situations. By accounting for these timeintervals, it is possible for the control software to adjust thetransition times for importing and exporting to increase the return forthe user. In other words, the control algorithm may account for themanner in which metering is performed by the system when determining theoptimal action of the energy storage system.

In exemplary embodiments it should be understood that the controlalgorithm and/or optimizer may take into account the behavior, operationand/or measurement techniques of the meter, battery management system,battery, and/or inverter when optimizing (or improving) the behavior ofthe energy storage system. The control algorithm and/or optimizer mayalso take into account the behavioral constraints, operationalconstraints or measurement constraints of the meter, battery managementsystem, battery, and/or inverter when optimizing the behavior of theenergy storage system.

FIG. 1 is an exemplary embodiment of a system for managing an energystorage system. In particular, FIG. 1 is an exemplary embodiment of asoftware environment but as would be understood by persons of skill inthe art, software, hardware and combinations of the two may be utilizedto build the systems and devices described herein. In FIG. 1, the system100 comprises an analysis environment 102. The analysis environment 102may be implemented as a server side portion of the system and inexemplary embodiments, may be responsible for developing customerspecific control models in conjunction with an appropriately trainedperson (data analyst/control engineer).

As discussed elsewhere herein, the system includes the design of a setof customer-specific models for managing and/or controlling the energystorage system at the customer premise.

The analysis environment 102 may be thought of as a “sand box” fordeveloping/crafting (in many different ways) customer-specific models orcomputational graphs for customer control models. The analysisenvironment 102 may be where data is analyzed, algorithms are trained,transformations are developed, parameters are defined, and/or items areconfigured. As inputs, the analysis environment 102 may utilizecustomer-specific data, including, for example, the location of aspecific customer, their system configuration, etc. In exemplaryembodiments, this data may be obtained from a customer relationshipmanagement (CRM) system 104, discussed below. The analysis environment102 may also utilize time-series data, including, for example: (a)historical energy market data 112 (e.g., energy price fluctuations,etc.); (b) environmental data 114 (e.g., sun/cloud conditions perregion, temperature, wind speed and direction, etc.); and (c) and otherdata 116.

As its output, the analysis environment 102 may produce acustomer-specific graph/model that contains e.g., the final executablecode to implement the control system on the customer's energy storagesystem. The executable code may be deployed to the client device andmetadata about the customer specific model and the executable code maybe stored in the EC (Embedded Compute) data database 106.

In exemplary embodiments, a time series database 108 may be utilized tocollect and store data from a variety of different sources including,for example: electricity usage and generation (e.g., solar, wind, etc.)from the commercial or residential premises, energy markets information,information from electricity networks (transmission and distribution)and utilities, weather services, financial data including (e.g., macroand micro economic data, etc.), and/or other indicator variables thatinclude, but are not limited to, time of day, day of week/month/year,market period, etc.

In exemplary embodiments, after data is collected it may be cleanedand/or sanitized to validate that it is complete (i.e. at theappropriate time intervals) and sane (i.e. is it within appropriatebounds). For example, in certain embodiments, server and/or clientprograms that run on the servers may either poll for new data or send itdirectly via standard web based protocols. The data may be checked usingsome predefined rules and then any missing or “bad” data (e.g., datathat doesn't meet the rules) may be replaced using data replacementrules. Data can be received in different serialization formats and maybe stored in a serialization format by the databases (e.g., both timeseries and SQL). In exemplary embodiments, the underlying value of datamay not be changed unless it is missing or “bad”.

In exemplary embodiments, the output of the control system/optimizer maybe sent to the time series database 108 and it is also possible to usethat data to provide visual (or audible) indications to a user. Forexample, in exemplary embodiments, the data may be utilized to determinea color and/or intensity of certain lights, or other types of display,that exist on the embedded compute controllers. This allows the systemto create an ambient display showing the status of the battery, solarenergy, usage etc. as well as possibly giving suggestions to consumersabout whether they should be saving or using energy at that time.

In exemplary embodiments, the CRM (and/or time-series database) 104 maystore details about customers, their energy usage and other metrics, andconfiguration and characteristics of their energy storage systems andenergy generation capabilities. The information may also include theiraddress, contact details, electricity retailer and retail contract,network provider and network contract, energy production system (whereapplicable), energy storage system (where applicable), etc. In exemplaryembodiments, two commercial or residential premises next door to oneanother might have vastly different control systems in place so thephysical locations of the two may be important.

In exemplary embodiments, the systems and devices may further comprisesignaling systems 110. When data becomes available to the system (e.g.,changes in the spot price from a centralized energy market), it may bedesirable to immediately transfer the data to the modules/processes ofthe customer's system that require the data. In other words, a marketevent may be sent to the centralized servers where it may be publishedout to the controllers so they can update what the storage system iscurrently doing. The signaling system may be configured to accept datain any form it is available from outside systems (e.g., from centralizedenergy markets, distribution network companies, etc.), convert it intoan appropriate form, and then send the data out using a variety ofdifferent protocols to other system modules/processes. In exemplaryembodiments, the signaling system 110 may be a data translation and/ordata packaging system.

As illustrated, the system may also include the energy storage system(ESS) 118. In exemplary embodiments, the ESS may contain one or more ofenergy storage cells or batteries, battery management systems,inverters, meters, etc.

FIG. 1 also illustrates interaction of the network service providerssystems 120 with the system. In exemplary embodiments, the networkservice providers may comprise systems or processes operated by networkservice providers (e.g., distribution or transmission providers). Thesesystems may include distribution management systems, demand response(DR) systems, dispatch systems, etc. and the systems described hereinmay interact with these network service providers to enable services forthese network service providers. In exemplary embodiments, theseservices may include, energy and power to avoid constraints on thenetwork at peak usage or times of the day and/or the ability to avoidreplacing aging infrastructure by dynamically offsetting loads andgenerators by utilizing an energy storage system.

FIG. 1 also illustrates the interaction of the system with marketsystems 122. Market systems are systems or processes operated by anenergy market. For example, these systems may include bidding andsettlement systems, etc. The systems, methods, and devices describedherein may interact with these systems to enable services for the energymarket. In exemplary embodiments, these services may comprise theprovision of energy (kWh), real and/or reactive power (kW/kvar) orancillary services (typically power (kW)).

The EC data database 106 may contain information about the executablecode that runs on the data table engine 200. As discussed above, theinformation stored in the EC data database 106 was created in theanalysis environment 102.

In exemplary embodiments, the EC data database may comprise differenttypes of data. For example, in embodiments, the data may includeparameters. Parameters are uniquely named objects that containinformation about a particular source of data. In exemplary embodiments,parameters may be uniquely mapped to time series database metrics.Another type of data that may be stored in the EC data database includestransformations. Transformations are methods that transform oneparameter into another. For example, a given transformation mightconvert the current temperature (a parameter) and wind speed (anotherparameter) into apparent temperature (a third parameter).Transformations can do other things with data as well, which may includesending the data onwards to other systems, setting values in the energystorage system, etc.

Another type of data that may be stored in the EC data database includesconfiguration items. Configuration items are typically derived from theCRM system that are used to parameterize transformations and parameters.For example, a configuration item might contain information about thewholesale market region that a given energy storage system purchasespower from and/or details about the energy storage systemsspecifications at a given residence and/or details about the time of usetariff for retail/network contracts.

In exemplary embodiments, the system may also comprise post operationsettlement and analysis tools 124. Specifically, during (or after) aclient control system is running it is necessary to understand theperformance and settlement revenues that have arisen because of theoperation. In exemplary embodiments, these tools may be software (e.g.,scripts) that convert data about the operation of an energy storagesystem into information about settlement revenues, performance, etc. Inexemplary embodiments, these tools may also allow for the visualizationof operational data.

The data table engine 200 is the core operational analysis and controlportion of the system. In exemplary embodiments, the data table engine200 may be responsible for executing the control model and correspondingcode that is created in the analysis environment 102. In exemplaryembodiments, the data table engine 200 may have four modes ofoperation—historical, accelerated real time simulation, real timedeployed, and cloud deployed (real time simulation).

In historical mode, the data table engine 200 may calculate the economicvalue that an energy storage system can deliver (from savings andadditional revenue streams) at a connection point in the electricitynetwork using historical data in bulk analysis mode (i.e. all data isavailable before analysis is undertaken).

In accelerated real time mode, the data engine table 200 may simulatecontrol of an energy storage system in order to deliver savings andadditional revenue streams at a connection point in the electricitynetwork. In exemplary embodiments, the control system may use historicaldata but simulate real-time data flows, i.e., presenting the data to thesystem as though it is coming in real time.

In real time deployed mode, the data table engine 200 controls an energystorage system in order to deliver savings and additional revenuestreams at a connection point in the electricity network.

In cloud deployed mode, the data table engine 200 may operate in amanner that is similar to the real time deployed mode but operates incloud based servers rather than deployed to an embedded processor at theenergy storage system. In exemplary embodiments, this may allow eitherreal-time simulation or can be used to control energy storage systemswith an internet enabled control system.

As discussed above, in exemplary embodiments, the control system may beoperated from a central location and the commands may be sent to theindividual energy storage systems in a distributed fashion. In exemplaryembodiments, the distributed operation may comprise sending instructionsto individual energy storage systems or to groups of systems such asthose systems in a certain geographic region. Additionally, it is notnecessary that each individual system comprise an individual meter. Inexemplary embodiments, meters may be centralized, partially centralizedor completely decentralized.

In the cases above the goal may be to generate a control signal (e.g.,charge/discharge decisions) for the energy storage system. In exemplaryembodiments, this control signal may directly determine the state ofcharge of the energy storage system, as well as determining the net flowof energy into or out of the electricity network at the connectionpoint. In exemplary embodiments, the system may use a financialoptimization algorithm to determine what to do at various points intime. For example, as prices (i.e., wholesale energy prices) becomeavailable, the optimizer may use that information to determine whichaction is most valuable and then implement that action through thecontrol system.

In exemplary embodiments, customers in the CRM 104 have a uniqueidentifier and the data table engine 200 may operate on behalf of asingle customer. Accordingly, for every customer there may be at leastone data table engine 200 running depending on which operational modes(of the four listed above) are currently in use.

FIG. 2 is an exemplary embodiment of a data table engine 200 for usewith a system for managing energy storage systems. As illustrated, thedata table engine 200 consists of three main components—a messenger 202,a computational graph 204, and a data store 206.

In exemplary embodiments, the messenger 202 may be implemented in any ofa plurality of manners (i.e., how it accepts data being sent to it orhow it retrieves data when in historical mode). The messenger 202 is theprimary interface to devices outside of the system. In exemplaryembodiments, the messenger 202 may be configured to know how to listenfor events/data (e.g., based on data retrieved from the EC data database106). The messenger 202 may listen for multiple events simultaneously(including internally sent messages from within the computationalgraph). In exemplary embodiments, for each event that the messenger 202is listening for, it may have an event handler registered. The eventhandler may receive the event, process it, and then make that data(e.g., wholesale energy prices) available for processing with thecomputational graph.

The computational graph 204 is where the control system is primarilyimplemented. In exemplary embodiments, there may be a plurality of codemodules that run within the computational graph. The order ofcalculation of the code modules may be determined by first building agraph that links modules together as specified by their input and outputparameters.

Accordingly, in exemplary embodiments, the control system may be acombination of the graphical structure of the transformations andparameters, both of which are parameterized by the configuration data.This approach may allow the system to ensure that each date table engine200 is running a unique control system for each customer, while ensuringthat it is not necessary to individually write a new control system foreach deployment. Since code modules can be reused and reordered indifferent systems this may be an efficient manner of building uniquecontrol systems for large numbers of operational units.

The data store 206 is an internal store of all of the data that has beendelivered to the data table engine 200 (e.g., from events or fromreading from the energy storage system, etc.) or that has been derived(e.g., transformed) from this data. When data arrives from an event itis stored in the data store and then after each transformation thatparameter value is also stored. Periodically the data store maysynchronize this data with an external database so the system has arecord of all operational data. This type of a backup may be useful forany number of reasons such as being able to recreate past instances ofthe data. Data compression techniques may be used to provide lossless orlossy compression in case of bandwidth constraints between the datatable engine and the external database.

In exemplary embodiments, the systems, methods, and devices may becapable of controlling the battery in real-time and may have one or moreof the following capabilities:

-   -   1. Receives code modules and algorithms designed for each        specific energy storage system from the analysis environment.    -   2. Uses a proprietary computational graph methodology to        optimize real time performance.    -   3. Collects (or sends) periodic data from the electricity market        and other required sources (through market interaction systems,        the central database system and other local (e.g., electricity        meter) and remote sources). This data may include but is not        limited to:        -   a. Price signals in the wholesale energy market or ancillary            services market.        -   b. Price signals in the distribution network.        -   c. Operational conditions (e.g., capacity, faults, etc.) in            the transmission or distribution networks.        -   d. Generation capacity, constraints, and faults of            generators connected to the transmission or distribution            networks.        -   e. Operational requirements, consumption, or usage patterns            of an individual residence or premises connected to the            distribution network.        -   f. Price signals in the retail contract of an individual            residence or premises connected to the distribution network.        -   g. Environmental factors (e.g., temperature, wind speed,            etc.)        -   h. Contracts for energy storage services.    -   4. Utilizes the data as inputs for running code modules and        algorithms in the deployed controller from the analysis        environment.    -   5. Utilizes the code modules and algorithms to determine the        value of commanding an action and/or magnitude (e.g., charge,        discharge, do nothing) for the storage system for each of the        many possible services each energy storage system is delivering.    -   6. Determines an ‘optimal’ action (e.g., charge, discharge, do        nothing) and/or magnitude for the energy storage system over a        finite time horizon.        -   a. The optimization process may automatically take into            account the implications of delivering multiple services            simultaneously. For example, provision of service A may            reduce the ability to physically provide service C.        -   b. The trading algorithm may also calculate opportunity            costs because the provision of a service now may preclude            the provision of services later—after all there are energy            storage constraints such as an empty or full battery.    -   7. Communicates bids for some market services to the market        interaction systems, as necessary.    -   8. Executes instructions to energy storage systems in order to        achieve revenue streams anticipated during the optimization.    -   9. Logs local measured data of what actually happened.    -   10. Saves the outputs of code modules and algorithms and uploads        them to a centralized data repository where further processing        and performance analysis may take place.    -   11. The data table engine may operate without communications        capabilities using an automated and proprietary redundancy        system.

In exemplary embodiments, the data table engine may respond to eventsfrom a variety of sources including, for example, market pricing events,events from the analysis environment or from local systems/hardware.

In exemplary embodiments, trading algorithms may be updatedremotely—e.g., incremental improvements may be made at the centralcompute which deliver higher expected returns or less risk to returns.

In exemplary embodiments, the system may be aware of characteristicsunique to the battery systems including, for example, response time andramp rates and exploit this knowledge during the optimization phase.

In exemplary embodiments, the analysis environment may comprise acentral computational, storage and optimization facilities that may beused to:

-   -   1. Retain a copy of data sent to, and received from, the remote        controller. This may include an automated data provenance        capability to ensure that the remote controller devices are        executing the correct code, as well as ensuring that the energy        storage systems are being operated in a manner that allows them        to generate revenue equal to that predicted by prior        simulations.    -   2. Analyze the value of operating energy storage, delivering an        arbitrary combination of energy (kWh) and capacity (kW) services        simultaneously, at a given location, residence or premises and        with performance properties of the battery, power electronics,        connection point, geographical location, installed or        prospective loads, installed or prospective generation, and any        other connection point, or connection point supporting        information to be accurately calculated from historical data        without installing an energy storage device.    -   3. Automatically generate optimization code modules and        algorithms for the energy storage systems based on analysis of        historical data or from data received from remote controllers in        the field to ensure it can generate revenue equal to that        predicted by prior simulations.        -   a. This can include automating the development of new            trading algorithms prior to deployment then remotely            uploading them to each embedded compute in each distributed            energy storage system.        -   b. The quantity of historical data may naturally increase            over time allowing the trading algorithms to be better            trained. The trading algorithms may be trained on a            significant part of the data then tested on the remaining            historical data. The omission of some of the historical data            in the training process allows for a more realistic test of            the trading algorithm's performance on real data it has            never seen before. The training of the algorithms may be            resource-intensive and may thus be done in the central            computer system.    -   4. Transform the rules, regulations, financial value and        engineering constraints inherent in the provision of a specific        electricity grid service (a contract) into a programming code        module with a common interface.    -   5. Dynamically add and remove electricity grid service provision        to remote controller devices in response to new and expired        contracts.    -   6. Calculate (probabilistically) available kWh and kVA at a        point in time, in response to predicted usage (with confidence        interval), predicted generation (with confidence interval),        predicted weather, predicted electricity system state, etc. at a        specific connection point (virtual storage provided by        uncontrolled, but predictable load and generation).    -   7. Calculate (probabilistically) available kWh and kVA at a        point in time depending on the predicted or measured        availability of an electric vehicle (EV) at a specific        connection point. Can predict when the EV will be available, and        how many kVA/kWh will be available upon connection.    -   8. Synthesize kWh and kVA available at a point in time from the        aggregate probabilistic kWh and kVA available from a collection        of independent electricity storage systems, demand response and        other demand side resources.    -   9. Automate modeling of storage performance changes with time,        temperature, kWh though, etc.—able to cater for differing        chemistries, power electronics, etc., but take a        whole-of-storage-system perspective

In exemplary embodiments, the control system may be configured toreceive sufficient data after turning on or rebooting such that thesystem can recover its previous state. For example, in exemplaryembodiments, if the control system requires a reboot, the system may beconfigured such that it receives a sufficient amount of data to recoverits state before the reboot, or at least substantially recover itsstate.

While embodiments of the system have been shown and described herein, itwill be obvious to those skilled in the art that such embodiments areprovided by way of example only. It is intended that the followingclaims define the scope of the invention and that methods and structureswithin the scope of these claims and their equivalents be coveredthereby.

1. A method for controlling an energy storage system, the methodcomprising: receiving time-series data and customer specific data;developing a customer specific control model based at least in part onthe time-series data and/or the customer specific data; training thecustomer specific control model; and deploying the customer specificcontrol model to the customer for use by the customer to determine whichof a plurality of modes the energy storage system should be in.
 2. Themethod of claim 1, wherein the plurality of modes of the energy storagesystem comprise any combination of a charging mode, a discharging mode,and a do nothing mode.
 3. The method of claim 2, wherein the pluralityof modes of the energy storage system further comprise an indication ofthe desired net flow of power and/or energy into or out of the energystorage system.
 4. The method of claim 1, wherein the customer specificcontrol model is configured to maximize a financial return from theenergy storage system by selling energy back to the network atbeneficial times as determined by the customer specific control model.5. The method of claim 1, wherein the customer specific control modulecan be updated by communicating an update to the customer.
 6. The methodof claim 1, wherein the control model utilizes customer configurationinformation to determine which of a plurality of modes and/orcorresponding magnitudes the energy storage system should be in.
 7. Themethod of claim 1, wherein a different customer specific control modelis developed for different customers.
 8. The method of claim 1, whereinthe time-series data comprises any combination of historical energymarket information, environmental information, electricity usage and/orgeneration from the commercial or residential premises, weatherservices, and/or financial data related to macro and micro economicinformation.
 9. The method of claim 1, wherein the customer specificdata comprises any combination of the location of the customer, thecustomer preferences, electricity retail contract information, networkprovider information, electrical details about the connection point,and/or information about the energy storage system.
 10. The method ofclaim 1, wherein the data is validated prior to being utilized todevelop the customer specific control model.
 11. The method of claim 1,wherein the energy storage system comprises at least one battery system.12. The method of claim 1, wherein the customer specific control modelcomprises a plurality of modules which are configured to be executed ina predetermined manner based, at least in part, on a linked relationshipbetween the modules and output parameters.
 13. A system for controllinga plurality of energy storage systems associated with a correspondingplurality of customers, the system comprising: at least one storagedevice configured to store time-series data and customer specific datafor a plurality of customers; at least one centralized processorconfigured to: receive the time-series data and the customer specificdata for a plurality of customers; (ii) develop a customer specificcontrol model for the plurality of customers based at least in part onthe time-series data and the customer specific data of the correspondingcustomer; and (iii) train the customer specific control model; aplurality of controllers associated with a corresponding plurality ofcustomer energy storage systems, wherein the plurality of controllers isconfigured to receive a corresponding customer specific control model todetermine which of a plurality of modes the energy storage systemcorresponding to the customer should be in.
 14. A system for controllinga plurality of energy storage systems associated with a correspondingplurality of customers, the system comprising: at least one storagedevice configured to store time-series data and customer specific datafor a plurality of customers; at least one centralized processorconfigured to: (i) receive the time-series data and the customerspecific data for a plurality of customers; (ii) develop a plurality ofcustomer specific control models for the plurality of customers based atleast in part on the time-series data and the customer specific data ofthe corresponding customer; and (iii) train the plurality of customerspecific control models; a plurality of controllers associated with acorresponding plurality of customer energy storage systems, wherein theplurality of controllers is configured to receive a correspondingplurality of customer specific control models to determine which of aplurality of modes the energy storage system corresponding to thecustomer should be in.
 15. The system of claim 13, wherein the pluralityof modes of the energy storage system comprise any combination of acharging mode, a discharging mode, and a do nothing mode.
 16. The systemof claim 15, wherein the plurality of modes of the energy storage systemfurther comprise an indication of the desired net flow of energy into orout of the energy storage system.
 17. The system of any of claim 13,wherein the customer specific control model is configured to maximize afinancial return from the energy storage system by selling energy backto the network at beneficial times as determined by the customerspecific control model and other services.
 18. The system of claim 14,wherein the customer specific control module can be updated by pushingan update to the customer.
 19. The system of claim 14, wherein thecustomer specific control model utilizes customer configurationinformation to determine which of a plurality of modes the energystorage system should be in.
 20. The system of claim 14, wherein thetime-series data comprises any combination of historical energy marketinformation, environmental information, electricity usage and/orgeneration from the commercial or residential premises, weatherservices, and/or financial data related to macro and micro economic andother data. 21-25. (canceled)